A segmental HMM based trajectory classification using genetic algorithm

• Published in: Expert systems with applications 2018-03, Vol.93, p.169-181
• Main Authors: Saini, Rajkumar, Pratim Roy, Partha, Prosad Dogra, Debi
• Format: Article
• Language: English
• Subjects: Classification; Datasets; Expert systems; Genetic algorithm; Genetic algorithms; HMM; Kernels; Markov chains; On-line systems; Recognition; Segmental HMM; Segments; Shrinkage; Signature recognition; Supervised learning; SVC2004; Trajectories; Trajectory classification
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2017.10.021

•An improved multi-kernel using Convex Hull and Douglas Peucker algorithm is proposed.•Classification is done with a two-stage HMM method using Global and Segmental HMM.•The combination of two-stage HMM classification is done using a genetic algorithm.•Experiments have been performed using two public datasets. Trajectory classification techniques face various challenges due to varying length and lack of the presence of clear boundaries among the trajectory classes. To overcome such challenges, a trajectory shrinking framework using Adaptive Multi-Kernel based Shrinkage (AMKS) can be used. However, such a strategy often results in over-shrinking of trajectories leading to poor classification. To improve classification performance, we introduce two additional kernels that are based on convex hull and Ramer–Douglas–Peucker (RDP) algorithm. Next, we propose a supervised trajectory classification approach using a combination of global and Segmental Hidden Markov Model (HMM) based classifiers. In the first stage, HMM is used globally for classification of trajectory to provide state-wise distribution of trajectory segments. In the second stage, state-wise trajectory segments are classified and combined with global recognition performance to improve the classification results. Combination of Global HMM and Segmental HMM is performed using a genetic algorithm (GA) based framework in the final stage. We have conducted experiments over two publicly available datasets, popularly known as T15 and MIT. We have achieved 94.80% and 96.75% of accuracies on T15 and MIT datasets, respectively. We also analyzed the robustness of the proposed framework by adding Gaussian noise. To show the effectiveness of the system, we have performed recognition of on-line signature using proposed Segmental HMM based combination model. In SVC2004 signature dataset, it outperforms traditional HMM-based systems.